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NEUROPSYCHOLOGIC FUNCTIONING IN CHILDREN WITHAUTISM: FURTHER EVIDENCE FOR DISORDERED COMPLEXINFORMATION-PROCESSING

Diane L. Williams1, Gerald Goldstein2, and Nancy J. Minshew11 University of Pittsburgh School of Medicine

2 VA Pittsburgh Healthcare System and University of Pittsburgh

AbstractA wide range of abilities was assessed in 56 high-functioning children with autism and 56 age- andIQ-matched controls. Stepwise discriminant analyses produced good group discrimination forsensory-perceptual, motor, complex language, and complex memory domains but lower agreementfor the reasoning domain than previously obtained for adults. Group discrimination did not occur forattention, simple language, simple memory, and visuospatial domains. Findings provide additionalsupport for a complex information-processing model for autism, previously based on adult data,demonstrating a pattern across domains of selective impairments on measures with high demandsfor integration of information and sparing when demands were low. Children as compared to adultswith autism exhibited more prominent sensory-perceptual symptoms and less pronounced reasoningdeficits reflecting brain maturation.

Autism is a behaviorally defined syndrome that is based on a triad of signs and symptomsinvolving social, language-communication-imaginative play, and restricted and repetitivebehavior and interests. Over the past two decades, there has been a growing appreciation thatother areas or domains of cognitive and neurologic functioning beyond this diagnostic triadare integrally involved in this syndrome (Baron-Cohen, Leslie, & Frith, 1985;Behrmann et al.,2006;Klin, Sparrow, de Bildt, Cicchetti, Cohen, & Volkmar, 1999;Minshew & Goldstein,2001;Ozonoff et al., 2004). For example, studies of motor abilities have documented a rangeof problems with motor praxis, motor planning, and imitation that now appear to be an integralelement of this syndrome (see review in: Dawson & Watling, 2000;Rogers, Hepburn,Stackhouse, & Wehner, 2003). The view of autism as an amnesic disorder of memory has beenrefuted (Bowler, Matthews, & Gardiner, 1997;Minshew & Goldstein, 1993;Renner, Klinger,& Klinger, 2000); however, an entire area of research describing the unique features of memorydysfunction in autism has evolved (Bennetto, Pennington, & Rogers, 1996;Minshew &Goldstein, 2001;Mottron, Morasse, & Belleville, 2001;O’Shea, Fein, Cillessen, Klin, &Schultz, 2005). Only a few studies, primarily survey-based, have been published about thesensory issues in autism, but these suggest that disturbances in this area are also elements(Baranek, Foster, & Berkson, 1997;Kientz & Dunn, 1997;Meyer & Minshew, 2005;Rogers,Hepburn, & Wehner, 2003;Rogers & Ozonoff, 2005). Neurologic studies of the postural control

Address correspondence to Nancy J. Minshew, Webster Hall, Suite 300, 3811 O’Hara Street, Pittsburgh, PA 15213. Tel: (412) 246-5485.Fax: (412) 246-5470. E-mail: [email protected] research was funded by a grant from the National Institute of Child Health and Human Development (NICHD) (U19HD35469) toNancy J. Minshew, which is part of the NICHD/NIDCD Collaborative Programs for Excellence in Autism (CPEA). Support was alsoprovided by a grant from NIDCD (K23DC006691) to Dr. Williams. The Medical Research Service, Department of Veterans Affairs isalso acknowledged for support of this research. We thank our participants and their families for the generous contribution of their timeand effort to this project.

NIH Public AccessAuthor ManuscriptChild Neuropsychol. Author manuscript; available in PMC 2007 February 22.

Published in final edited form as:Child Neuropsychol. 2006 August ; 12(4-5): 279–298.

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system (reviewed in: Minshew, Sung, Jones, & Furman, 2004) and the oculomotor system(Goldberg, Lasker, Zee, Garth, Tien, & Landa, 2002;Takarae, Minshew, Luna, & Sweeney,2004) have demonstrated involvement of neural systems outside those previously thought tobe involved on the basis of traditional behavioral observations. Models of autism must expandconceptually to consider broader cognitive, neurologic, and brain involvement in autism.

We previously proposed a model that has the capacity to accommodate impairments in autismbeyond the diagnostic triad as well as to incorporate the emerging results of functional magneticresonance imaging (fMRI) studies. According to this model, autism is a selective impairmentin the neural processing of complex information across domains and sensory modalities, withintact or enhanced simple abilities in the same domains as impairments (Minshew & Goldstein,1998;Minshew, Johnson, & Luna, 2001;Minshew, Sweeney, & Luna, 2002). In this model,complexity is a proxy for the level of demand placed on the brain’s processing capacity bytasks or situations. Cognitive or neurologic function is compromised when the processingdemands placed on the brain’s systems exceed their capacity. A breakdown in processingoccurs in autism when the information to be handled is inherently complex or becomes complexdue to its amount or time constraints. Therefore, individuals with autism exhibit impairedperformance on complex or higher order tasks that are well within the capability of individualsof their general ability level, e.g., they have selective impairments in higher order processingnot explained by their general ability level. At the same time, individuals with autism canperform simpler skills in the same domains as impairments as well as or even better than peers.Hence, the concept of complexity has more to do with the effect on the brain’s mechanismsduring processing of information than it does with the type of information (i.e., social orlanguage) per se.

A major consideration in the choice of the term for this model for autism was the need for itto apply to the motor, sensory, postural control, and oculomotor systems, as well as to cognitionand language. We also wanted terminology that was resonant with that of neurophysiological,functional imaging, structural imaging and neuropathological studies. If findings from theinvestigation of autism are to be integrated across methodologies, a model and terminology isneeded that facilitates this integration. Information-processing is a term highly familiar toneuroscientists, neurophysiologists, neurologists, and cognitive psychologists. The term“complex” is not optimal because of its imprecision at capturing the dynamic nature of theimpairment, but it is useful for describing the shared characteristic of the impairments acrossdomains. Another possible choice such as “higher order abilities” does not reference the keylink to neural processing and the dynamics of the task demands and situations imposed on thebrain. Terms such as “integration” or “computation” do not have the meaning that“information-processing” does across the range of scientific disciplines engaged in the studyand treatment of autism. Regardless of the term chosen for the model, the emphasis should beon the concepts that it represents.

The complex information-processing model of autism was originally based on aneuropsychological profile study of 33 individually matched pairs of typical controls and high-functioning adolescents and adults with autism (Minshew, Goldstein, & Siegel, 1997). Thisstudy was designed to objectively investigate hypotheses as to whether various sensoryperceptual, attention, memory, or language impairments were present individually as singleprimary deficits or whether there was evidence that many symptoms co-occurred in the sameindividuals with autism. If the latter were true, the key questions from a neurologic perspectivewere, first, what feature or characteristic did the impairments have in common and, second,what feature did the intact abilities have in common.

The neuropsychological battery used in the original profile study was designed to includemeasures of multiple aspects of attention (encoding, sustained attention, selective attention,

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attention to extrapersonal space, focused attention, and intramodal shifting of attention),elementary sensory and higher cortical sensory perception, elementary motor and motor praxisabilities, multiple aspects of auditory and visual memory, oral and written language functionsranging from phonetics to text comprehension, problem solving, and the rule-learning, conceptformation, and flexibility aspects of abstraction. These measures were selected to test thehypothesized core neuropsychological deficits of eight different primacy arguments that hadbeen proposed up to that point, as well as our own hypothesis that autism was a disorder ofhigher order abilities dependent on neocortical systems (see Minshew et al., 1997 for a completelisting of these). The division of abilities into subdomains was possible when there were a largenumber of measures available to assess performance within a domain. When these divisionswere made, they respected known conventions in the respective disciplines. When the numberof measures was small, measures were retained in a single domain and performance within thedomain was evaluated to determine if there was a dissociation between simple and complexabilities. In the sensory perceptual domain, elementary or simple sensory perceptual abilitiescorresponded to the sensory abilities supported by the spinothalamic tracts and posteriorcolumns, whereas higher cortical or complex sensory perception referred to cortically mediatedperception. This distinction between elementary and higher cortical sensory perceptual abilitiesis conventional to behavioral neurology, reflecting the complexity of abilities and theirdifferent localizations in the nervous system. A similar distinction was made betweenelementary motor movements and motor praxis. The specific hypothesis was that if neocorticalsystems were involved, there would be motor praxis problems. With regard to memory, adistinction was made between memory tasks that involved organizing strategies, either theiruse or detection, (16 word list on the California Verbal Learning Test or the Rey OsterreithComplex Figure) and memory tasks that relied solely on basic associative processes (paired-associate word learning or three-word short-term memory). In the case of language, a divisionwas made between formal language (decoding of words and nonwords, spelling, vocabulary,fluency) and interpretative language abilities, which are considered emergent skills (languageabilities not reducible to the component elements of words and rules of grammar). Within thecategory of reasoning, conventional neuropsychological distinctions were made, separatingattribute identification, rule-learning and concept formation, with flexibility being a reflectionof the completeness of concept apprehension. Hence, the selection and characterization ofmeasures as tests of simple (basic or elementary) or complex (higher order) abilities was basedon neuropsychological or neurological considerations.

The resulting neuropsychological profile of the adult autism group was characterized by poorerperformance than the age-and IQ-matched control group on higher order or skilled motor,interpretive language, memory for complex material (requiring an organizing strategy or thedetection of inherent organization in the material to support recall), and concept formationtasks (Minshew et al., 1997). Within the sensory perceptual domain, differing performancewas seen between tests of elementary and higher cortical sensory perception tests, though themixed performance resulted in lack of significance for the domain as a whole. Because theautism group was matched to the control group on Full Scale, Verbal, and Performance IQ andage, the individuals with autism performed poorer in these areas than would be predicted bytheir age and overall cognitive ability. Their poorer performance could not be attributed simplyto task difficulty, as task difficulty is typically a function of IQ and there were no IQdiscrepancies between the two groups nor were all the tests on which they did poorly dependenton IQ. For example, motor and sensory tasks have low correlations with IQ. The pattern ofperformance was also not readily explained by lack of effort, as some tasks that they did poorlyon, such as Verbal Absurdities or Picture Absurdities, required minimal effort and others thatthey did well on, such as the Continuous Performance Test or the cancellation tasks, requiredconsiderable effort. Scores on tasks within the simple language domain also discriminatedbetween the two groups; however, the discrimination occurred because the adults with autismperformed better than the controls on measures of phonetic analysis and spelling.



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The one exception to the pattern of differing performance within domains on simple andcomplex tasks was that individuals with autism and normal controls did equally well on bothsimple and complex visuospatial tasks. It should be noted that many aspects of visuospatialprocessing such as mental rotation, second order motion, and complex constructional taskswere not assessed. Importantly, the traditional complex visuospatial stimulus inneuropsychologic assessment is face recognition and this was not studied until later. Manystudies have since reported impairments of this ability in autism (de Gelder, Vroomen, & vander Heide, 1991;Gepner, de Gelder, & de Schonen, 1996;Joseph & Tanaka, 2003;Klin et al.,1999;McPartland, Dawson, Webb, Panagiotides, & Carver, 2004;Schultz, et al., 2000). Inaddition, other complex visuospatial tasks have been reported to be impaired in individualswith autism (Bertone, Mottron, Jelenic, & Faubert, 2003,2005;Shah & Frith, 1983;Spencer,O’Brien, Riggs, Braddick, Atkinson, & Wattam-Bell, 2000).

The results of the neuropsychologic profile study with adults (Minshew et al. 1997) providedsubstantive evidence that autism is not the result of a single primary deficit; rather, it has asimultaneous impact on many domains. The common denominator of the impairments inautism across domains and modalities appeared to be the high processing demands; thecommon denominator of the intact abilities appeared to be involvement of low-level cognitiveor neurologic processes. Symptoms were most prominent in those domains that placed thehighest demands on higher order processing and integration of information—the social,communication, and reasoning areas. However, signs and symptoms were also present onhigher order tasks in other domains—the sensory, motor, and memory domains—and theseshould be considered integral parts of the disorder of autism.

Because autism is a developmental disorder, any proposed model must explain not only themanifestation of the disorder in the adult population but also what occurs during thedevelopmental process. It was not known whether the neuropsychological profile describedby Minshew et al. (1997) in adults with autism is characteristic of autism throughoutdevelopment or whether it is the result of a developmental process.

Numerous research studies in children with autism have focused on a single cognitive domainthat has been proposed as the primary, or core, deficit in autism. Such views are based on thehypothesis that very early biological damage in a module of the developing brain causes adisruption in the neurodevelopmental process (Leslie, 1991). According to this temporalprimacy hypothesis, a single deficit appears during childhood before all other deficits and isinstrumental in generating the additional deficits that emerge as the child matures (Bruinsma,Koegel, & Koegel, 2004;Zwaigenbaum, Bryson, Rogers, Roberts, Brian, & Szatmari, 2005).For example, an early lack of motivation to socialize in children with autism couldhypothetically lead to deficits in social orienting, joint attention, emotion perception, affectivesharing, and imitation (Dawson et al., 2004)1. Earlier primary deficit models included sensoryimperception (Ornitz, 1983), inattention to extrapersonal space (Dawson & Lewy, 1989),difficulty with shifting attention (Courchesne et al., 1994), and memory dysfunction(Bachevalier, 1991,1994;Bauman & Kemper, 1994;Boucher, 1981). These deficits werehypothesized to compromise the capacity to develop language, memory, conceptual reasoning,and other related higher order skills.

Other models of the neurobehavioral basis of autism have proposed primary deficits incognitive processes or modules that are associated with a particular cortical region or regions.This approach assumes that autism is the result of a deficit within a particular neural system.

1These gaze abnormalities are sometimes assumed to be abnormalities in elementary attention. However, they are complex behavior andlikely to represent a composite of functions. In neurology, the social use of gaze is a right frontal hemisphere function and it is likely thatthese early gaze abnormalities in autism will be understood as the result of interactions between neural systems.



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Candidate systems that have been extensively studied are the executive function or dorsolateralprefrontal cortex network and the theory of mind or medial frontal network. The executivefunction model of autism was predicated on the observation that individuals with autism havedifficulty with several cognitive skills that are thought to be mediated by the prefrontal cortexsuch as response inhibition, working memory, cognitive flexibility, planning, and fluency(Geurts, Verté, Oosterlaan, Roeyers, & Sergeant, 2004;Hill, 2004;Pennington & Ozonoff,1996;Edgin & Pennington, 2005). The theory of mind model of autism originated frombehavioral studies that demonstrated that individuals with autism had difficulty inferring whatother people were thinking (Baron-Cohen et al., 1985). Neuroimaging work with theory ofmind tasks has identified a “mentalizing” network (medial prefrontal cortex, superior temporalsulcus at the temporo-parietal junction and temporal poles) that is less activated in individualswith autism (Castelli, Frith, Happé, & Frith, 2002). Thus, these models propose that autism isrelated to disturbances in a single neural system.

The Minshew et al. (1997) study was completed with older adolescents and adults with autism,therefore, an earlier occurring deficit in a single modality or domain that influenced thedevelopment of the other domains could not be excluded. If the complex information-processing model of autism is viable, this pattern of impairments should be seen at earlier pointsof development. Therefore, it is necessary to evaluate individuals with autism at the youngestage at which a comprehensive neuropsychologic battery can be administered. Although not asdefinitive as a longitudinal study of children followed to adulthood, a child study that parallelsthe previous adult study could characterize the neuropsychologic profile at an earlier stage ofdevelopment.

Although neuropsychological measures are not as ideal as experimental measures carefullyconstructed to control for levels of processing, they provide a reasonable first approach for thesystematic measurement across a large number of domains in a single sample. The aim of thepresent study was to test our proposed model by assessing children with autism, compared toage-and IQ-matched controls, using age-appropriate measures that correspond to those fromthe previously reported Minshew et al. (1997) study of adults with autism.

METHODParticipants

The autism group was composed of 56 children with autism (46 males and 10 females) and 56controls (39 males and 17 females). The groups ranged in age from 8 to 15 years. Demographicand psychometric data are presented in Table 1. As was the case for the adult study, werestricted the sample to individuals with high-functioning autism, (e.g., those with IQs of 80or above) and age 8 and older to assure that the participants could fully cooperate forpsychological testing and were unlikely to have the numerous additional disorders associatedwith low-functioning autism. Another consideration in setting the lower limit of the IQ scoreat 80 was to ensure that typically developing control participants could be identified for thestudy. The lower age limit of 8 years was also chosen, first, to ensure that participants withautism were sufficiently verbal and had enough capacity to complete a thorough battery and,second, so that relatively uniform measures could be used across this sample.

All participants in the affected group met the cutoffs for autism on the Autism DiagnosticObservation Schedule (ADOS: Lord et al., 1989;Lord et al., 2000) for the Reciprocal SocialInteraction, Communication, and Total algorithm scores. In addition, all participants withautism met cutoffs for autism on the Autism Diagnostic Interview-Revised (ADI-R; Le Couteuret al., 1989;Lord, Rutter, & Le Couteur, 1994) for Reciprocal Social Interaction,Communication, and Restricted, Repetitive, and Stereotyped Behaviors and had abnormaldevelopment before 3 years of age. The ADI-R assesses developmental history and reported



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current functioning based on an interview with a caregiver. The diagnosis of autism establishedon the basis of the ADI-R and ADOS was verified by expert opinion [NJM] or [DLW] basedon accepted clinical descriptions of high-functioning individuals with autism (Minshew,1996;Minshew & Payton, 1988;Rapin, 1991;Rutter & Schopler, 1987). A subject with ADI-Rand ADOS scores above the cutoffs for autism could be ruled out on the basis of expert opinion,but expert opinion could not override ADI-R or ADOS scores that fell below the cutoff. Allparticipants with autism communicated in complete spoken sentences and did not havebehavioral problems that prevented them from completing testing. The participants with autismdid not have any associated or causative genetic, neurologic, or infectious conditions, were ingood medical health and had no history of seizures, birth injury, or head trauma. Theparticipants with autism were community volunteers recruited through advertisements innewsletters, postings on autism-related websites, and presentations for parents andprofessionals (NJM and DLW). The sample was based upon consecutive admission of allreferrals meeting the inclusion criteria.

Controls were community volunteers recruited through advertisements in neighborhoods withthe same socioeconomic level as the families of origin of the participants with autism. Theywere prescreened by completing a questionnaire on demographic information and family andpersonal history of medical, neurological, and psychiatric disorders. Inclusion criteria includedgood physical health, no regular medication use, and good peer relationships based on parentor self-report and staff observations during eligibility testing. Exclusion criteria included apersonal history of neuropsychiatric disorders, learning disability or brain insults prior to orafter birth, and a family history in first-degree relatives of developmental cognitive disorders,mood, and anxiety disorders, and autism in first-, second-, and third-degree relatives.

The University of Pittsburgh Medical Center Institutional Review Board approved the study.Procedures were fully explained to all participants and to their parents or guardians. Writteninformed consent was obtained from their parents or guardians and written assent was obtainedfrom the children. All participants were recruited through the Subject Core of the Universityof Pittsburgh Collaborative Program of Excellence in Autism funded by the National Instituteof Child Health and Human Development.

Test BatteryThe tests and variables used are presented in Table 2 with means and standard deviations ofthe obtained scores. Tests were chosen from available measures for child cognitive andneuropsychological assessment to represent the same domains and modalities evaluated in theadult study and the same range of abilities within domains. The assignment of a test to a domainwas made on the same basis as it was for adults, and was consistent with the generally acceptedclassification system used in neuropsychological assessment (Lezak, 1995). In some cases, thesame test used in the adult study could be used because a child version of the test was available.In other cases, we had to choose tests that were roughly equivalent. For example, the WideRange Assessment of Memory and Learning (WRAML; Sheslow & Adams, 1990) was usedas the major memory test to replace the two memory measures used in the adult study—theWechsler Memory Scale-Revised (Wechsler, 1987) and the California Verbal Learning Test(Delis, Kramer, Kaplan, & Ober, 1987). Child versions of these instruments were not availableat the time the current study was initiated. We retained the three-word short-term memoryprocedure (described in Ryan, Butters, Montgomery, Adinolfi, & Didario, 1980) and the firsttrial (with 6 choice points) of a stylus maze-learning task in the simple memory domain. Thelatter is an unpublished, experimental task used in our previous research with the adult sample.We also retained the Nonverbal Selective Reminding Test (NVSRT; Fletcher, 1985) in thecomplex memory domain. We replaced the Wechsler Adult Intelligence Scale-Revised



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(Wechsler, 1981) subtests with the equivalent Wechsler Intelligence Scale for Children-III(WISC-III; Wechsler, 1991) subtests in the appropriate domains. The 20 Questions task (Olver& Hornsby, 1966) and Stanford-Binet Intelligence Scale (4th Ed.) Picture Absurdities test (S-B IV; Thorndike, Hagen, & Sattler, 1986) used in the adult study as measures of reasoningwere also used in this study. The Problem Situations and Plan of Search Tests from the S-BIV (Thorndike et al., 1986) were included as additional measures of reasoning and problem-solving ability. The sensory perception domain measures were the same as the ones used inthe adult study—the Luria-Nebraska Tactile Scale (Golden, Purisch, & Hammeke, 1986) andthe finger agnosia and fingertip number writing tasks from the Halstead-ReitanNeuropsychological Test Battery (Reitan & Wolfson, 1993). The attention domain includedthe Digit Span subtest from the WISC-III (see Lezak, 1995, p. 360) and two measures that hadbeen used with the adults—the Continuous Performance Test (CPT; Conners, 1995) and theNumber Cancellation task (Mesulam, 1985). In the motor domain, we retained two of the adultmeasures—finger tapping (Reitan & Wolfson, 1993) and the grooved pegboard (Matthews &Klove, 1964)—and added two new measures—the WISC-III Coding subtest (a measure ofpsychomotor processing speed) and a measure of grip strength in the dominant hand. Thesimple language domain included measures from the Wide Range Achievement Test 3(Wilkinson, 1993), the Woodcock Reading Mastery Tests-Revised (WRMT-R; Woodcock,1987), a verbal fluency measure (Spreen & Strauss, 1998), and the WISC-III Vocabularysubtest. Two subtests from the Test of Language Competence-Expanded (TLC-E; Wiig &Secord, 1989), the Passage Comprehension subtest from the WRMT-R, Verbal Absurditiesfrom the S-B IV, and an Oral Directions subtest from the Detroit Test of Learning Aptitude(DTLA-2; Hammill, 1985) comprised the complex language domain.

We did not obtain full samples for some of the measures; sample sizes for each measure areindicated in Table 2. Missing data occurred largely because the participant could not cooperatefor the test. Because the group sizes were still substantial for these tests, and the subsampleswere still age- and IQ-matched, we did not exclude them from the multivariate analyses. Twotests from the adult study—the Wisconsin Card Sorting Test (Heaton, 1981) and the HalsteadCategory Test (Reitan & Wolfson, 1993)—were administered at the beginning of the study;however, results in both the autism and control groups indicated performance was not validbelow age 15. These two tests were discontinued and were, therefore, not included in the childbattery.

Trained neuropsychology technicians, under the supervision of a clinical neuropsychologist,administered the test battery over several test sessions. Length of test sessions and breaks weretailored to each participant’s needs so that test performance was representative of participantability. It typically took 2 to 3 days (5 to 6 sessions) to complete the testing.

Data AnalysisThe statistical method used to analyze the pattern of deficits was Wilks’ stepwise discriminantanalysis, as was the case in the adult study. We employed Shutty’s (1991) recommendationthat preliminary direct method analyses should be performed prior to using stepwiseprocedures. In the stepwise method, the most discriminating variable, typically the one thatproduces the highest F-ratio, is entered first, followed by other variables that combine with thefirst variable in a way that increases discriminatory accuracy. Variables are entered or removeduntil a preestablished tolerance test is failed indicating that additional entry of availablevariables would make no further contribution to discriminative accuracy. For this study, theF to enter was 1.0, and the F to remove was .95. This method generates a final classificationmatrix that provides the percentage of correct classifications (autism or control) and thepercentages of true and false positives and negatives. The statistical significance of the



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classification matrices was evaluated with kappa, a coefficient of agreement for nominal scales.Relatively high kappas indicate that the variables chosen to represent a domain discriminatedwell between the participants with autism and the controls, whereas low kappas indicate thereverse. According to the Landis and Koch criteria (1977), a kappa of .40 or greater indicatesfair to good agreement beyond chance. Examination of variables entered and not enteredprovided an indication of the nature of the variables within each domain that discriminatedbest between the participants with autism and the controls.

Before analyzing the data, we considered using logistic regression as it has been suggested thatthis may provide more accurate solutions. However, based on a paper by Press and Wilson(1978) that indicated that in most cases logistic regression and discriminant analysis yieldsimilar solutions and to remain consistent with the adult study, we used discriminant analysisfor the final analyses. Preliminary logistic regression analyses with the present data did, in fact,yield similar solutions.

RESULTSThe results of the stepwise discriminant function analyses are presented in Tables 3 and 4.Table 3 presents data concerning entry of variables and general classificatory accuracy. Table4 contains the classification matrices for the various domains that provide informationconcerning percentages of true and false positives and negatives. Percentages were usedbecause of variations in sample sizes across domains.

Using the Landis and Koch criteria, fair to good agreement beyond chance that the domainsdiscriminated between the autism and control groups (a kappa of .40 or higher) was obtainedfor the sensory perceptual, motor, complex language, and complex memory domains. All ofthe complex language measures passed the tolerance test for the children, reflecting the centralrole of problems with interpretative language in autism. The kappa of .35 obtained for thereasoning domain approached the discriminatory level. The attention, simple language, simplememory, and visuospatial domains were not found to discriminate between the autism andcontrol groups beyond chance levels. Within domains without prior determined simple andcomplex subdomains, the tests that failed to pass the tolerance test generally represented lowerlevel skills within that domain. For example, within the sensory perceptual domain, the positionsense and sharp-dull discrimination scores failed the tolerance tests; the measures that passedall reflected higher cortical tactile functions, such as form recognition. Within the motordomain, the Coding subtest from the WISC-III, a measure of psychomotor or processing speedthat requires integrative functioning, passed the tolerance test.

Comparison to Adult ResultsA comparison of the results from the current study of children with our previous study of olderadolescents and adults with autism can be made by examining the kappa scores from that study,which, for ease of comparison, are provided in Table 3 for each of the domains. The kappascores show that the neuropsychological profile for the children with autism was quite similarto that of the adults with a few notable exceptions. As in the adult study, tests of attention,simple memory, and visuospatial abilities failed to discriminate between the autism and controlgroups and tests of motor, complex memory, and complex language did discriminate betweenthe two groups. Unlike the adults, in the sensory perceptual domain, the child group showeda significant impairment. There was no superior performance for the simple language domainin the children. The children with autism performed slightly better than the controls on themeasure of spelling (but this did not meet statistical significance), but performance on the othermeasures in this domain was comparable to that of the controls. In the adult study, the kappa



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for the reasoning domain was well within the fair to good range; this domain yielded a kappajust below the range of fair discrimination in the current child study.

DISCUSSIONThe present study of 56 children with high-functioning autism and 56 matched typical controlsreplicates and expands the findings of our previous study of neuropsychologic functioning inadults with autism. Moreover, the child data provide evidence that the neuropsychologic profileobserved in adults is present earlier in life. These data fail to support the hypothesis of a singleprimary cognitive deficit as a cause of the multiple deficit pattern observed in either the childrenor the adults.

The children with autism exhibited involvement of multiple domains and a differential patternof impairment between simple and complex skills within the affected domains of sensoryperception, motor, language, and memory. The children with autism had difficulty on tasksthat placed the highest demands on integration of information, e.g., memory for large amountsof material or complex material and comprehension of text. At the same time, the children withautism did not differ significantly from age- and IQ-matched controls on elementary or basiccognitive skills within these same domains, e.g., associative learning, vocabulary, andspelling.2 The children in the autism group also did not differ from the controls on attentionand visuospatial domains. Lower agreement occurred for the reasoning domain than previouslyobtained for the adults.

Thus, at the earliest age that a comprehensive battery of this type could be administered, thecognitive profile in autism was consistent with a reduction or constraint in information-processing that differentially impacted the major domains and abilities within those domains.The most affected domains were those that placed the highest demands on information-processing resources. Within affected domains, the skills affected were likewise those thatplaced the most demands on integration of information.

Some notable differences between the results of the adult and the child studies were obtained.Although caution must be exercised in drawing longitudinal conclusions from cross-sectionaldata, it does appear that there is a more pronounced impairment in sensory perceptual abilitiesin childhood in autism. In addition, it appears that these impairments become attenuated, atleast to the point that the measures employed failed to yield significant domain differences, inlater adolescence and adulthood. The lack of significance in the adult group for this domainmay reflect developmental amelioration over time in the sensory symptoms, e.g., improvedintegration within the sensory system at the cortical level as occurs to some extent with otherabilities during the second decade. An actual greater sensory disturbance in children would beconsistent with the findings reported in the small sensory literature of the prominence ofsensory symptoms in children, the frequent use of sensory interventions in children for calming,and the comparable mixed-sensory-perceptual findings in the adult neuropsychological profilestudy of Rumsey and Hamburger (1988). Studies of sensory gating or other cortical sensorymechanism as a function of age may shed light on the changes in the sensory profile withmaturity in autism.

In the adult study, the simple language domain had a significant kappa, reflecting betterperformance of the autism group than individually matched controls. This was not seen in the

2Basic skills, in addition to attentional and visual skills, are the basis for performance on IQ tests. This pattern of results may explainwhy IQ scores can be in the normal range in verbal children with autism despite striking impairments in high-order skills and adaptivebehavior.



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present study of children. The reason for this difference in simple language performancebetween the adults and the children is not clear; it may represent a decrease in performance inadulthood for both groups with the adult autism group maintaining more of their spelling andphonetic analysis abilities than the adult controls. The major point of the simple languagedomain finding, in both the adult and child studies, is that those individuals with autism whobecome verbal do not demonstrate deficits in basic or formal language skills and cannot bedifferentiated from IQ-matched controls on this basis. Formal language skills are the basis uponwhich others automatically estimate an individual’s ability level. In the case of individuals withautism, their formal language skills are misleading predictors of their higher order or complexlanguage abilities. Consequently, they are at high risk that those speaking to them areoverestimating what they comprehend.

Another difference in the results of the children and the adults was in the reasoning domainwith adults having a kappa score in the good range of agreement and children having a kappabelow the fair range (.35). The variability in the children’s scores was apparently too great tocarry the domain to the high level of discriminatory accuracy found in the adults. Part of thereason for this lack of discrimination in the children may be the lack of equivalency in measuresfor this domain across the two studies. Whereas both studies used the S-B IV PictureAbsurdities and the 20 Questions task, the other two measures in this domain differed betweenthe studies. In addition, the Wisconsin Card Sorting test had to be dropped from the child testbattery due to the inability on the part of many participants to complete this test. As a result,the reasoning measures used in the child study may have been less able to detect a possibledifference in reasoning and problem solving. Alternately, neither the children with autism northe typically developing controls may have reached the level of development that allowed themanifestation of the reasoning impairments in autism. Other research has shown that the keyfeatures of the abstraction-executive function impairment in autism relate to inflexibility (Hill,2004;Kleinhans, Akshoomoff, & Delis, 2005;Ozonoff, Strayer, McMahon, & Filloux, 1994)and self-initiated concept formation (Minshew, Meyer, & Goldstein, 2002). Typical childrendo not attain these abilities until the frontal lobes mature in the second decade of life.Comparative studies of children and adolescents with autism have shown that reasoning andstrategy deficits emerge during the adolescent years as typical children develop these skillsand the children with autism do not (Minshew, Meyer, & Goldstein, 2002).

One obvious question that could be raised about this profile and its interpretation is why thetests were not rank-ordered by complexity, independent of domains. From a purely cognitiveperspective, it might be assumed that any simple language task is more complex than any motortask; however, this rank ordering might not be valid from a neural perspective. The organizationof abilities and tests into domains used in this study reflects the basic neurobiologic principlethat different cognitive and neurologic functions are subserved by separate neural systems inthe brain. These biologically based boundaries were respected. A limitation of this study is thatthere are domains affected by autism that were not assessed with this battery. The most obviousare the social domain and the nonverbal language (communication with gaze or eyes, faceexpression, prosody, and body language) domain. Whereas these domains were not assessedwithin the neuropsychologic battery, they were clearly affected in the children with autism inthe present study, as evidenced by their scores above cutoffs in these domains on the ADOSand ADI-R.

Another limitation of this study is that, due to the demands of the neuropsychological measures,it was confined to children 8 years of age or older. It is possible that a study of children withautism younger than 8 years would reveal involvement of a single domain rather than multipledomains. However, research to date has not provided support for this hypothesis. Even at firstdiagnosis, which is about 18 months, there is involvement of multiple domains. Studies have



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reported evidence of motor and sensory processing impairments in infants later diagnosed withautism (Baranek, 1999;Teitelbaum, Benton, Shah, Prince, Kelly, & Teitelbaum, 2004). Thus,at an early age there is evidence of impairments outside of the traditional diagnostic triad forautism. Another theoretical consideration against a single primary deficit model is that it is noteasy to explain sensory, motor, and memory impairments as the outcome of early jointattention, gaze, or social impairments.

Both the adult and child studies were completed with individuals with IQs in the normal range.It has not been empirically shown that lower functioning individuals with autism evidence apattern of intact simple and impaired complex information-processing. However, elements ofthis pattern were confirmed by the studies of Fein and colleagues (1996). Theoreticalapplication of the model to the lower functioning group with autism predicts that, as autismseverity increases, total information-processing capacity would be reduced, but there wouldbe a disproportionate reduction in higher order skills and disproportionate preservation of lowerorder skills relative to the overall loss in information-processing capacity. This would lead tomental retardation but of a type that retains the features of autism. From a neurobiologicperspective, as the severity of autism increases, there would be a progressive shrinkage ofhigher order circuitry and increased reliance on lower order circuitry until all connectivitybetween primary sensory motor cortices and association cortex was lost. Thus, whereas themodel has been formulated with high-functioning individuals with autism, it could be extendedto explain the symptoms in lower functioning individuals with autism including theunexplainable high rate of co-occurrence of mental retardation with autism.

Emerging results from functional neuroimaging studies with individuals with autism alsoprovide supportive evidence for the complex information-processing model. This modelpredicted that neural underconnectivity would be the analogue of deficits and that intactconnectivity or overconnectivity would be the analogue of intact or enhanced abilities.Evidence of this type is being provided by functional magnetic resonance imaging studies oflanguage comprehension (Just, Cherkassky, Keller, & Minshew, 2004) and problem solving(Just, Cherkassky, Keller, Kana, & Minshew, 2006). The proposed “underconnectivity theory”of autism is the biological extension of the complex information-processing model (Just et al.,2004). Another fMRI study has shown that individuals with autism compensate for deficienthigher order cognitive abilities and circuitry by using basic cognitive processes and posteriorbrain regions (Koshino, Carpenter, Minshew, Cherkassky, Keller, & Just, 2005). An additionalfMRI study, which has been particularly enlightening with regards to this profile, investigatedvisual perception by the cortex and concluded that differences in sensory perception were“likely related to higher level cognitive areas, and were the result of top downprocesses” (Hadjikhani et al., 2004). These neuroimaging studies have provided furthervalidation of the findings of the neuropsychologic profile.

The above described fMRI studies have provided evidence of functional underconnectivity inhigher order circuitry and intact lower order circuitry, analogous to the cognitive patternobserved in the present study. Structural morphometry of the brain in autism has yieldedevidence of enlarged brain volume beginning postnatally and likely preceding the onset of thesymptoms of autism (Courchesne et al., 2001;Hazlett et al., 2005;Sparks et al., 2002). Thisearly overgrowth is thought to disrupt the normal histologic processes associated with braindevelopment and the emerging connectivity of cortical gray and white matter. Therefore,although fMRI studies are demonstrating functional underconnectivity, the volumetricanalogue of this brain disturbance is an increase in volume. In an extension of this work,Akshoomoff et al. (2005) reported that variability in cerebral and cerebellar size is correlatedwith functioning level in children with autism. More correlations between fMRI and brain



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structure and between brain structure/function and behavioral indices of autism can be expectedto occur.

It is important to recognize that in the interval between the publication of the adult profile studyand the present study there has been considerable research in autism that has documented thedomain specific impairments observed in both studies. The unique contribution of the childprofile study at this juncture is that the assessments of multiple domains were completed in thesame participant sample with the key questions of, first, what critical feature the impairmentsshare across domains and, second, what critical feature the intact abilities share. The answersappear to be 1) that the acquisition of information is intact; 2) that there is an overall constraintor reduction in the capacity to process information; 3) that this constraint disproportionatelyimpacts higher order information-processing or the capacity to process information or materialwhen the demands of the task or situation are high; and, 4) that this processing constraint occursat a lower level than would be expected based on the individual’s age and IQ.

We have now provided evidence from a total of 89 individuals with autism and 89 matchedcontrols ages 8 to 40 years that documents a disproportionate impact of deficits on higher orderabilities and intact basic abilities in these same domains. Because the individuals with autismranged in IQ from 80 to 125 and were below the age of 40, they could be matched to typicallydeveloping healthy control individuals, ensuring that the findings were related to autism andnot to mental retardation in the autism group. These data provide substantive support for theproposed complex information-processing model of autism.

The complex information-processing model not only broadens the conceptualization of autismbeyond the diagnostic triad, it is also a working concept that facilitates the integration offindings across cognitive and neurobiologic methodologies. Such connections are essential inthe search for the neural basis and developmental neurobiologic mechanism of autism. Theanswers in this realm are likely to be a long way off, but the present findings suggest thatanswers lie in the mechanisms associated with the underdevelopment of higher order abilitiesacross domains rather than in a single neural system or single brain region.

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Table 1Demographic Data for Autism and Control Groups.

Autism Group n=56 Control Group n=56

M SD M SD p

Age 11.36 2.18 11.82 2.20 .26Years of Education 5.57 2.34 5.96 2.11 .38Verbal IQ 105.52 16.12 107.86 8.21 .34Performance IQ 102.07 14.63 105.98 8.40 .09Full Scale IQ 104.13 15.09 107.50 8.21 .14Male: Female 46:10 39:17



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Table 2Psychometric Data Used for Discriminant Analysis.

Autism Group Control Group

Tests entered into predictionequations [autism, control]

M SD M SD

Attention DomainWISC-III Digit Span (scaledscore) [55, 55]

10.05 3.09 10.73 2.84

Continuous Performance Test(mean reaction time correctresponses) [32, 35]

381.34 125.53 375.55 122.13

Number Cancellation(omissions) [39, 34]

12.92 9.62 16.21 13.44

Sensory perceptual DomainLN1 Simple Touch (raw score)[54, 56]

.41 .86 .21 .56

LN Stereognosis (raw score)[41, 55]

.51 .90 .18 .55

LN Sharp-Dull Discrimination(raw score) [53, 56]

1.08 1.28 .77 .99

LN Position Sense (raw score)[54, 56]

.11 .60 0.00 .00

Reitan-Klove—FingerAgnosia (raw errors) [32, 51]

.75 1.19 .65 1.66

Halstead-Reitan: FingertipNumber Writing (errors) [51,56]

12.73 9.21 5.79 6.28

Motor DomainFinger Tapping—dominanthand [52, 56]

39.15 10.21 43.68 7.04

Grooved Pegboard—dominanthand (time in seconds) [56, 56]

84.96 20.23 76.93 13.63

WISC-III Coding Scaled Score[56, 56]

8.38 3.53 10.66 2.86

Grip Strength—dominant hand(kilograms) [56, 56]

16.35 7.80 22.26 7.65

Simple Language DomainWISC-III Vocabulary (scaledscore) [56, 56]

11.14 3.18 11.27 1.96

WRAT Reading (standardscore) [54, 55]

108.07 14.57 106.84 11.02

Verbal Fluency Test—FAS(number of words) [44, 36]

25.27 10.22 27.06 10.66

Woodcock Reading Mastery—Word Attack (standard score)[52, 40]

108.23 13.97 108.45 9.11

WRAT Spelling (standardscore) [54, 55]

111.572 16.27 106.60 10.67

Complex Language DomainTLC Figurative Language(scaled score) [47, 40]

7.38 3.16 9.93 2.64

TLC Making Inferences (scaledscore) [45, 41]

8.44 3.06 9.59 2.97

Woodcock Reading Mastery—Passage Comprehension(standard score) [53, 40]

101.45 11.65 105.30 7.74

S-B IV Verbal Absurdities (rawscore) [56, 56]

8.25 3.85 11.95 2.67

Detroit Oral Directions (scaledscore) [42, 36]

9.60 2.84 10.67 1.69

Simple Memory Domain3 Word Short Term Memory(number of correct sequences)[43, 36]

3.37 2.55 3.50 2.74

Maze Learning Trial 1 Errors[32, 36]

3.88 1.52 3.67 1.51

WRAML Verbal Learning(scaled score) [55, 55]

9.96 3.52 11.62 2.65

WRAML Sound Symbol(scaled score) [46, 43]

11.28 3.20 10.95 2.85

WRAML Visual Learning(scaled score) [46, 43]

10.98 3.09 11.16 2.52

Complex Memory Domain



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Autism Group Control Group

Tests entered into predictionequations [autism, control]

M SD M SD

WRAML Verbal Learning—Delayed (scaled score) [53, 56]

9.19 3.64 10.38 2.73

WRAML Visual Learning—Delayed (scaled score) [46, 43]

9.35 3.19 9.47 3.03

WRAML Picture Memory(scaled score) [47, 43]

8.79 3.05 10.26 2.74

WRAML Design Memory(scaled score) [47, 43]

8.53 3.19 10.60 2.51

Nonverbal SelectiveReminding—Consistent Long-Term Retrieval [28, 31]

29.25 17.49 36.68 15.78

WRAML Finger Windows(scaled score) [46, 44]

8.98 2.58 11.23 3.21

Reasoning Domain20 Questions (% constraintseeking) [55, 56]

45.27 21.49 57.54 13.58

S-B IV Picture Absurdities(raw score) [43, 37]

25.00 3.38 26.30 2.80

S-B IV Problem Situation 1 &2 [54, 56]

1.98 1.12 2.68 .86

S-B IV Plan of Search [45, 56] .36 .484 .66 .478Visual-Spatial DomainWISC-III Block Design (scaledscore) [56, 56]

11.75 3.62 11.27 2.92

WISC-III Object Assembly(scaled score) [56, 56]

10.43 3.25 10.07 1.87

WISC-III Picture Completions(scaled score) [56, 56]

10.80 3.08 11.14 2.35

1LN = Luria-Nebraska Battery.

2Autism group had a mean score higher than control group.



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Table 3Discriminant Analysis Results by Domain for Child & Adult Profile Studies.

Domain Tests Failing Tolerance Test Tests Passing ToleranceTest (in order of entry)

% CorrectClassificationfor Children

Child k Adult1k

Attention WISC-III Digit Span,Continuous Performance Test

Number Cancellation 53.4 .06 .33

Sensory perceptual LN Position Sense; LNSharp-Dull

HR Fingertip # Writing; LNStereognosis; LN Simple

Touch; RK Finger Agnosia

73.8 .442 .29

Motor Finger Tapping; GroovedPegboard

Grip Strength; WISC-IIICoding;

73.2 .46 .52

Simple Language WISC-III Vocabulary WRATReading; Verbal Fluency

Test;

WRAT Spelling; WRMT-RWord Attack

55.6 .12 .42

Complex Language None Verbal Absurdities; TLCFigurative Language; TLC

Inferences; Detroit OralDirections; WRMT-R

Passage Comprehension

73.2 .47 .45

Simple Memory 3 Word Short Term Memory;Maze Learning, Trial 1;

WRAML Verbal Learning;WRAML Sound Symbol

WRAML Visual Learning 51.7 .04 .30

Complex Memory WRAML Verbal Learning-Delayed

WRAML Picture Memory;WRAML Finger Windows;NVSRT—Consistent LongTerm Retrieval; WRAML

Visual Learning—Delayed;WRAML Design Memory;

72.4 .45 .55

Reasoning Picture Absurdities Plan of Search; TwentyQuestions Problem

Situations

67.3 .35 .52

Visual-Spatial WISC-III PictureCompletion, Block Design,

Object Assembly3

53.6 .07 .12

1Adult kappas are provided to allow comparison of results within each domain (although tests included within a domain may have differed by age group).

2Bolded kappas indicate discrimination between the control and autism group for that age group.

3The direct entry method was used for this domain allowing for the production of a classification table and computation of kappa.



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Table 4Percentages of Correct Classifications by Discriminant Function Analyses for the CognitiveDomains.

Predicted Group

Domain Actual Group Autism Control

Attention AutismControl

61.555.9

38.544.1

Sensory perceptual AutismControl

66.722.0

33.378.0

Motor AutismControl

69.623.2

30.476.8

Simple Language AutismControl

52.040.0

48.060.0

Complex Language AutismControl

71.124.2

28.975.8

Simple Memory AutismControl

45.741.9

54.358.1

Complex Memory AutismControl

45.741.9

54.358.1

Reasoning AutismControl

68.933.9

31.166.1

Visual-Spatial AutismControl

50.042.9

50.057.1



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